广州大气挥发性有机物的臭氧生成潜势及来源研究

2008年秋季在广州城区及其下风向沿海乡村地区采用活性炭吸附管采集大气挥发性有机化合物(VOC),应用二次热解吸-GC/MS联用技术测量56种VOC的大气浓度,研究在典型海陆风条件下VOC的组成特征和日变化规律,并采用臭氧最大增量及OH自由基反应活性两种方法估算了该地区各VOC的臭氧生成潜力,探讨了VOC优先控制物种及...     

大连金州地区海陆风及热内边界层研究

根据大连金州2009年常规气象站资料分析大连金州地区海陆风变化特征,并用MM5V3模式模拟典型日的海陆风风场变化和热内边界层位温场结构变化,结果表明:海风和陆风出现的频率有明显的季节性变化。冬季陆风较多,春夏海风较多,春季、冬季易形成海陆风;海风起止时间夏季长冬季短,陆风起止时间秋冬季较夏季长;典型海陆风日中,海风造成陆地湿度变大,海风风速大于陆风风速;通过海风的数值模拟,海风由生成到成熟海岸吹向内陆其厚度可增厚到1 000 m以上,伸向内陆距离可到18.9 km;热内边界层向内陆呈舌状分布,海岸边界层高度在700 m之间,抛物面高度随着向内陆延伸的距离增加而升高。热内边界层最高达1 000 m。     

青岛华威风电场风能资源分析

根据华威风电场2008年4月1日-2009年3月31日一整年的测风仪测风资料,对风电场的风能资源各参数进行了详细地计算和分析,对风电场的风能资源进行评价,为当地的风能开发提供分析基础。     

上海崇明地区大气分形态汞污染特征

2014年3月~2015年2月对上海崇明东滩湿地公园的气态零价汞(GEM)、活性气态汞(RGM)和颗粒态汞(PBM)分别进行了为期1a的连续监测.GEM、RGM和PBM的年平均浓度分别为(2.75±1.13)ng·m~(-3)、(13.39±15.95)pg·m~(-3)和(21.89±40.42)pg·m~(-3),明显高于对应北半球背景值浓度.GEM浓度在夏季最高(3.65 ng·m~(-3)),受自然源排放影响较大,秋冬季较低,受人为源排放影响较大;RGM浓度在春季最高,冬季最低,主要受风速风向的影响;PBM浓度在秋冬季节明显高于其他季节,受秋冬季节较多的细颗粒物重污染过程的影响.GEM和PBM浓度均夜间较高,白天较低,主要受空气气团混合作用影响.RGM浓度在下午较高,主要是由于GEM在下午的光氧化作用加强,利于RGM的生成.GEM和PBM浓度在偏西风向上浓度较高,受上海、江苏等地人为源排放影响较大.RGM浓度在东南风向上浓度明显高于其他方向,这是因为RGM主要来源为人为排放,其浓度受风速影响较大,东南风向上平均风速较小,持续的弱风及停滞的空气不利于RGM的扩散.     

2015年干季佛山一次重空气污染过程形成机理研究

2015年12月21-23日,广东珠三角佛山地区出现了一次PM_2.5重污染过程.利用佛山地区顺德、禅城、三水3个站点风廓线雷达、激光雷达和微波辐射计观测资料,结合地面气象观测数据和污染物浓度数据,分析研究了这次重污染过程的形成机理.结果表明：1形成这次污染过程的主要原因是近地层偏南风和偏北风对峙导致水平风速减小,大气水平输送能力变差;持续时间长且强度达到3℃·km^-1的强逆温抑制了污染物的垂直扩散;800 m以下超过90%的高相对湿度造成气溶胶粒子吸湿增长显著.2持续时间长且比较深厚的小风层是造成这次污染过程的直接原因,小风层厚度是预报空气质量变化的较好工具.与地面风速相比,PM_2.5浓度与小风层厚度的相关系数最多能提高0.36,且具有较长的预报时效.佛山地区小风层的风速阈值为3.8 m·s^-1.3这次污染过程存在两种不同的污染形成机制,污染前期（21-23日中午）主要以本地污染物累积为主,污染后期（23日下午）下风向地区（三水）的污染主要是受上风向地区（顺德和禅城）的污染输送影响.     

佛山地区干季边界层垂直风温结构对空气质量的影响

利用佛山地区2013年12月大气边界层观测试验得到的垂直风温资料和相应逐日AQI资料、逐时PM_(2.5)浓度资料,研究了佛山地区大气边界层垂直风温结构对空气质量的影响.结果表明:佛山地区干季持续存在的逆温结构是导致PM_(2.5)污染较重的重要原因.干季污染日近60%的最低逆温层高度低于1000 m,而非污染日低于1000 m的最低逆温层仅占36%,污染日佛山贴地逆温频率高达31.2%.逆温层出现高度较低,将污染物压缩积累在贴地层大气中导致污染较重.在大陆冷高压控制下,佛山地区的边界层结构演化非常典型,最大边界层高度和最大边界层通风量表现出了显著相关,污染日日平均边界层高度始终维持在较低的水平,多数时候不足500 m,最大边界层高度则大部分小于1000 m,日平均边界层通风量主要分布在500~1500 m~2·s~(-1)之间,在极端情况下甚至不足300 m~2·s~(-1),最大边界层通风量大部分处于1500~5000 m~2·s~(-1)之间,导致污染物始终聚集在较低的大气边界层内,使得PM_(2.5)浓度长时间维持在较高的污染水平.佛山地区风场存在显著的3层结构,较小的底层风速意味着大气的输送和扩散能力较弱,高度较低的中层使得污染物进一步被压缩累积在大气底层,垂直风场的不稳定性使得污染日佛山地区局地回流活跃,回流(RF)指数极小值多分布在0.2~0.4之间,污染日RF指数普遍小于非污染日,垂直风场的有效输送能力被显著削弱.     

覆冰输电塔-线体系风致动力响应分析

依据流体诱发振动原理,结合已有覆冰计算模型,考虑降雨的分类,模拟了输电塔-线体系不同高度导线的覆冰和风荷载。以东北地区某输电塔为例,对覆冰输电塔-线体系在稳定风速激励下的动力响应进行分析,并与不考虑塔线耦联的导线舞动分析结果及覆冰塔线拟静力的分析结果进行对比,对塔位移、输电线位移、钢材应力进行了分析。研究结果表明:塔-线耦联体系对覆冰导线风振有很大影响,覆冰输电塔抗风设计安全度需要进一步提高。     

Performance improvement of airfoils for wind blade with the groove

This study was a basic one to explore how much the aerodynamic characteristics of wind blade improve. The extent of improvement according to the shapes of groove placed on the surface of airfoil (NACA0015) was analyzed through computational analysis. A commercial computational fluid dynamics (CFD) code, the ANSYS Fluent 13, was used in this study. In this study, regarding with the positions and shapes of groove, the end of groove was placed at a certain distance (length, l) from both the front and back of separation starting point, the depth and the width were designated as h and d respectively. Analysis was conducted at the 7° angle of attack under the following conditions; the thickness (δ) of boundary layer to the depth (h) of groove ratio (h/δ) 0.6–1.0, the depth (h) of groove to the width (d) of groove ratio (h/d) 0.1–1.4, and the length (l) between the end of groove and separation point to the thickness (δ) of boundary layer ratio (l/δ) ?0.5–0.5. Among these conditions, the best improvement of lift to drag ratio, standing at 15.3%, was under h/δ = 1.0, h/d = 0.12, and l/δ = –0.5 (7° AOA, Re = 360k). In addition, throughout the range of angle of attack, 2–14°, lift to drag ratio improved by 0.8%, 5.1%, 3.2%, and 1.8% each when Reynolds numbers were 280k, 360k, 450k, and 530k. It is also confirmed that the shape of groove contributed to recovering velocity around airfoil wall and the lift to drag ratio improvements by groove were maintained at the given range of Reynolds number and around the angle of attack, 7°.     

The Relative Importance of Landscape Amenity and Health Impacts in the Wind Farm Debate in Australia

In recent years, Australia has experienced public debate around the growth in wind farms as part of government and community strategies to move from fossil fuels to renewable energy. Many of the arguments put forward by opponents and supporters are similar to those that have been evident in North America and Western Europe, including possible environmental impacts on wildlife, noise levels and, perhaps most prominently adverse changes in ‘landscape amenity’. A concern of increasing prominence is that of adverse human health impacts from proximity to operating turbines. Using analysis of submissions to public inquiries and a small number of detailed interviews, we consider the increased focus in Australia on the health impacts of wind farms. We note that health impacts, as elsewhere in the world, are often part of a suite of objections to wind farm developments but conclude that for some people near such developments, these are the main concern and not a cover or proxy for other concerns, such as changes in landscape amenity and aesthetics.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.